policy
A-Sharpe-ratio-Tournament-between-Deep-Reinforcement-Learning-algorithms
France-Cappe-code · PyTorch
or hover any field below to flag it
Overview
Name
A-Sharpe-ratio-Tournament-between-Deep-Reinforcement-Learning-algorithms
Author
France-Cappe-code
Framework
PyTorch
License
MIT
Skill type
other
Evidence level
untested
Task description
A Sharpe ratio based tournament. Agents (SAC, PPO, A2C, etc.) are trained in a StockTradingEnv with costs/limits and tech indicators (MACD, RSI). Dynamic allocation aims to maximize the Sharpe Ratio. This notebook is not a financial advisor.
Spaces
Action space
other · 0-dim · 0Hz
Observation space
- type: other
Links
HuggingFace repo
null
Paper (arXiv)
null
Compatible robots
20anybotics-anymal-cnot in seedalohanot in seedgoogle-barkour-vbnot in seedboston-dynamics-spotnot in seedfranka-fr3not in seedgoogle-barkour-v0not in seedagilex-pipernot in seedberkeley-humanoidnot in seedbitcraze-crazyflie-2not in seedanybotics-anymal-bnot in seedagility-cassienot in seedarx-l5not in seedbooster-t1not in seedfranka-emika-pandanot in seedfranka-fr3-v2not in seeddynamixel-2rnot in seedflexiv-rizon4not in seedassetsnot in seedapptronik-apollonot in seedfourier-n1not in seed
Compatible environments
0No environments list A-Sharpe-ratio-Tournament-between-Deep-Reinforcement-Learning-algorithms yet.
Datasets that reference this policy
0No datasets reference A-Sharpe-ratio-Tournament-between-Deep-Reinforcement-Learning-algorithms yet.